Gautam Pennathur

Brief communication: Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis

The emergence of multidrug resistant varieties of Mycobacterium tuberculosis has led to a search for novel drug targets. We have performed an insilico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen M. tuberculosis. Enzymes from the biochemical pathways of M. tuberculosis from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. We have identified six pathways unique to the pathogen M. tuberculosis when compared to the host H. sapiens. Potential drug targets from these pathways could be useful for the discovery of broad spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 185 distinct targets identified from these pathways, many are in various stages of progress at the TB Structural Genomics Consortium. However, 67 of our targets are new and can be considered for rational drug design. As a case study, we have built a homology model of one of the potential drug targets MurD ligase using WHAT IF software. The model could be further explored for insilico docking studies with suitable inhibitors. The study was successful in listing out potential drug targets from the M. tuberculosis proteome involved in vital aspects of the pathogens metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatic methods can be extended to other pathogens of clinical interest.

A novel medium for the enhanced cell growth and production of prodigiosin from Serratia marcescens isolated from soil

BackgroundProdigiosin produced by Serratia marcescens is a promising drug owing to its reported characteristics of having antifungal, immunosuppressive and antiproliferative activity. From an industrial point of view the necessity to obtain a suitable medium to simultaneously enhance the growth of Serratia marcescens and the pigment production was the aim of this work. The usage of individual fatty acid as substrate in industries would be cost-effective in the long run and this paved the way for us to try the effect of different fatty acid-containing seeds and oils of peanut, sesame and coconut as source of substrate.ResultsThe addition of sugars only showed slight enhancement of prodigiosin production in nutrient broth but not in fatty acid containing seed medium. The powdered peanut broth had supported better growth of Serratia marcescens and higher yield of prodigiosin when compared with the existing nutrient broth and peptone glycerol broth. A block in prodigiosin production was seen above 30°C in nutrient broth, but the fatty acid seed medium used by us supported prodigiosin production upto 42°C though the yields were lower than what was obtained at 28°C. From the results, the fatty acid form of carbon source has a role to play in enhanced cell growth and prodigiosin production.ConclusionWe conclude by reporting that the powdered and sieved peanut seed of different quality grades were consistent in yielding a fourty fold increase in prodigiosin production over the existing media. A literature survey on the composition of the different media components in nutrient broth, peptone glycerol broth and the fatty acid containing seeds and oils enabled us to propose that the saturated form of fatty acid has a role to play in enhanced cell growth and prodigiosin production. This work has also enabled us to report that the temperature related block of prodigiosin biosynthesis varies with different media and the powdered peanut broth supports prodigiosin production at higher temperatures. The medium suggested in this work is best suitable from an industrial point of view in being economically feasible, in terms of the higher prodigiosin yield and the extraction of prodigiosin described in this paper is simple with minimal wastage.

Tripeptide analysis of protein structures

BackgroundAn efficient building block for protein structure prediction can be tripeptides. 8000 different tripeptides from a dataset of 1220 high resolution (≤ 2.0°A) structures from the Protein Data Bank (PDB) have been looked at, to determine which are structurally rigid and non-rigid. This data has been statistically analyzed, discussed and summarized. The entire data can be utilized for the building of protein structures.ResultsTripeptides have been classified into three categories: rigid, non-rigid and intermediate, based on the relative structural rigidity between Cα and Cβ atoms in a tripeptide. We found that 18% of the tripeptides in the dataset can be classified as rigid, 4% as non-rigid and 78% as intermediate. Many rigid tripeptides are made of hydrophobic residues, however, there are tripeptides with polar side chains forming rigid structures. The bulk of the tripeptides fall in the intermediate class while very small numbers actually fall in the non-rigid class. Structurally all rigid tripeptides essentially form two structural classes while the intermediate and non-rigid tripeptides fall into one structural class. This notion of rigidity and non-rigidity is designed to capture side chain interactions but not secondary structures.ConclusionsRigid tripeptides have no correlation with the secondary structures in proteins and hence this work is complementary to such studies. Tripeptide data may be used to predict plausible structures for oligopeptides and for denovo protein design.

In silico identification of putative metal binding motifs

Metal ion binding domains are found in proteins that mediate transport, buffering or detoxification of metal ions. In this study, we have performed an in silico analysis of metal binding proteins and have identified putative metal binding motifs for the ions of cadmium, cobalt, zinc, arsenic, mercury, magnesium, manganese, molybdenum and nickel. A pattern search against the UniProtKB/Swiss-Prot and UniProtKB/TrEMBL databases yielded true positives in each case showing the high-specificity of the motifs. Motifs were also validated against PDB structures and site directed mutagenesis studies.

Molecular dynamics simulations of human and dog gastric lipases: Insights into domain movements

Mammalian gastric lipases are stable and active under acidic conditions and also in the duodenal lumen. There has been considerable interest in acid stable lipases owing to their potential application in the treatment of pancreatic exocrine insufficiency. In order to gain insights into the domain movements of these enzymes, molecular dynamics simulations of human gastric lipase was performed at an acidic pH and under neutral conditions. For comparative studies, simulation of dog gastric lipase was also performed at an acidic pH. Analyses show, that in addition to the lid region, there is another region of high mobility in these lipases. The potential role of this novel region is discussed.

Promoter addresses: revelations from oligonucleotide profiling applied to the Escherichia coli genome

BackgroundTranscription is the first step in cellular information processing. It is regulated by cis-acting elements such as promoters and operators in the DNA, and trans-acting elements such as transcription factors and sigma factors. Identification of cis-acting regulatory elements on a genomic scale requires computational analysis.ResultsWe have used oligonucleotide profiling to predict regulatory regions in a bacterial genome. The method has been applied to the Escherichia coli K12 genome and the results analyzed. The information content of the putative regulatory oligonucleotides so predicted is validated through intra-genomic analyses, correlations with experimental data and inter-genome comparisons. Based on the results we have proposed a model for the bacterial promoter. The results show that the method is capable of identifying, in the E.coli genome, cis-acting elements such as TATAAT (sigma70 binding site), CCCTAT (1 base relative of sigma32 binding site), CTATNN (LexA binding site), AGGA-containing hexanucleotides (Shine Dalgarno consensus) and CTAG-containing hexanucleotides (core binding sites for Trp and Met repressors).ConclusionThe method adopted is simple yet effective in predicting upstream regulatory elements in bacteria. It does not need any prior experimental data except the sequence itself. This method should be applicable to most known genomes. Profiling, as applied to the E.coli genome, picks up known cis-acting and regulatory elements. Based on the profile results, we propose a model for the bacterial promoter that is extensible even to eukaryotes. The model is that the core promoter lies within a plateau of bent AT-rich DNA. This bent DNA acts as a homing segment for the sigma factor to recognize the promoter. The model thus suggests an important role for local landscapes in prokaryotic and eukaryotic gene regulation.

Automatic classification of protein structures using physicochemical parameters.

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge.The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied.Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90–96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Understanding mutations and protein stability through tripeptides.

A novel methodology to predict the local conformational changes in a protein as a consequence of missense mutations is proposed. A pentapeptide at the locus of mutation plays the dominant role and it is analyzed in terms of tripeptides. A measure for spatial and temporal fluctuations in a pentapeptide is devised and validated. The method does not involve any prior knowledge of structural templates from sequence homology studies. Structural deformations can be predicted with about 70–80% reliability in any protein. Disease causing mutations and benign mutations have been addressed. In particular, p53, retinoblastoma protein and lipoprotein lipase are studied in detail.

Research article: Understanding the lid movements of LolA in Escherichia coli using molecular dynamics simulation and in silico point mutation

The Lol system in Escherichia coli is involved in localization of lipoproteins and hence is essential for growth of the organism. LolA is a periplasmic chaperone that binds to outer-membrane specific lipoproteins and transports them from inner membrane to outer membrane through LolB. The hydrophobic lipid-binding cavity of LolA consists of α-helices which act as a lid in regulating the transfer of lipoproteins from LolA to LolB. The current study aims to investigate the structural changes observed in LolA during the transition from open to closed conformation in the absence of lipoprotein. Molecular dynamics (MD) simulations were carried out for two LolA crystal structures; LolA(R43L), and in silico mutated MsL43R for a simulation time of 50 ns in water environment. We have performed an in silico point mutation of leucine to arginine in MsL43R to evaluate the importance of arginine to induce structural changes and impact the stability of protein structure. A complete dynamic analysis of open to closed conformation reveals the existence of two distinct levels; closing of lid and closing of entrance of hydrophobic cavity. Our analysis reveals that the structural flexibility of LolA is an important factor for its role as a periplasmic chaperone.

Resonance Energy Transfer between protein and rhamnolipid capped ZnS quantum dots: application in in-gel staining of proteins.

The interaction of proteins with quantum dots is an interesting field of research. These interactions occur at the nanoscale. We have probed the interaction of Bovine Serum Albumin (BSA) and Candida rugosa lipase (CRL) with rhamnolipid capped ZnS (RhlZnSQDs) using absorption and fluorescence spectroscopy. Optical studies on mixtures of RhlZnSQDs and proteins resulted in Försters Resonance Energy Transfer (FRET) from proteins to QDs. This phenomenon has been exploited to detect proteins in agarose gel electrophoresis. The activity of the CRL was unaffected on the addition of QDs as revealed by zymography.